Financial methodology to valuate and predict the news impact of major events on financial instruments

ABSTRACT

According to some embodiments, an event having an association with a financial instrument may be identified. The event may then be classified into at least one of a plurality of predefined event classes, each predefined event class being associated with a set of similar events. Media data associated with media coverage of the event may be retrieved and data elements may be extracted from the media data, wherein the data elements include at least one quantified communication parameter including at least one of a short term media coverage volume, a publication weight, a tonal balance, and an impact of available photographs. A prediction of the upcoming media coverage of the event may be generated, including a predicted volume and tonality of the upcoming media coverage, wherein said prediction is generated using a modeling computer system, a numerical model, said extracted data elements, and information about said predefined event class.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/308,886 entitled “Financial Methodology to Valuate andPredict the News Impact of Major Events on Financial Instruments” filedMay 22, 2006 which was based on, and claimed the benefit and priority toPatent Application Ser. No. 60/595,175, flied on Jun. 13, 2005. Theentire contents of those applications are incorporated herein byreference.

BACKGROUND OF THE INVENTION

The disclosed method concerns making predictions in respect of mediacoverage, product sales or stock price performance, following materialbusiness developments, positive or negative, such as the win/loss of amajor contract, a corporate crisis, an accidents, the discovery of aside-effect, or a significant court ruling. The method proceeds from therecognition that factorial analysis of the event (development) can helppredict how the news will be reported, which in turn can be used topredict the impact on stock price, or products sales, and othersignificant aspect of business performance.

Crisis-type stock price dynamics have been the subject of detailedanalysis and modeling in the past, both academic and commercial. This ishardly surprising; billions of dollars are lost or, in some cases,earned, in the highly volatile stock-trading that often follows thebreaking news of such sudden events. The input parameters of existingpredictive impact models are typically the financial attributes of theevent itself and the companies involved.

The disclosed method augments this approach with a new and fundamentallydifferent class of attributes; communication parameters which can beused to predict the media coverage of the event.

The approach can be illustrated with an example. Company X announces thetermination of a sponsorship contract with a high profile celebritywhose promotions of the company's products has been seen as effective. Afew days later the company disappoints their investors by announcing aquarterly loss, rather than the expected profit. The news wiresreportage mention the loss of the sponsorship in their reportage on themissed earnings target. Some of the reports are accompanied with a photoof the celebrity. Traditional market analysis will re-compute a new andlower price objective for the stock, based on lower earningsexpectations. Some potential or actual stock owners will learn of thenews through such financial analysis, but most will be exposed to thenews through media, social or traditional. Such media reportage is notonly a conduit for carrying factual and quantified information. Thecontextualization of the news, the sentiment of the reporting, thedegree of disbursement, the linkage to pubic concerns and many otherfactors all contribute to the change in confidence in the stock and thedemand for their products. These media coverage factors can, to someextent, be predicted, based on communication parameters around the news,such as the photo of the celebrity, and what other material is competingfor media space at the same time.

The ability to predict media coverage is valuable in three differentinterdependent layers, each valuable in itself.

-   -   In its most elementary form it can guide the PR response    -   Secondly, and building on no 1, it can be used to predict the        impact on product revenues    -   Thirdly, and building on no 2, on the confidence in a stock

The disclosed method proceeds from the recognition that the businessattributes of the event itself, and of the subject company, are oftenpoor predictors for the volume and tonality of the media coverage,through which medium the markets will be informed and kept up-to date ofthe event/crisis/change. It is not that these parameters are invalid,but they are insufficient. As the disclosure will demonstrate, a suddenevent or crisis and the corporation impacted by it, are associated withcommunication parameters as well as the business parameters already usedby existing prediction models. These communication parameters includefactors such as the quality of photographs, the level of convergencewith current public concerns or fashions and the level of positive ornegative endorsement by key influencers. The disclosed method permitsthe identification of such communication parameters for different typesof events and it provides quantification of both the influence on thenews coverage and on the resultant stock price movements productrevenues.

The central principle of the methodology is that the communicationfactor can be derived by a three-step process: a) identify all theparameters with high correlations against stock price movements orproduct sales b) identify all the parameters which have a highcorrelation against the media impact c) all the parameters which have ahigh correlation against both the media, revenues and stock price arethe ones with the highest predictive potential.

A related application (U.S. Provisional Patent Application No.60/595,175 filed on Jun. 13, 2005) has been filed for examining thenon-sudden but more sustained spread between analyst targets and stockprices by analyzing how investor confidence is influenced continuouslyby media coverage.

BRIEF SUMMARY OF THE INVENTION

Securities analysts and many investors employ quantitative valuationmodels of financial instruments. For readability the description in thisdocument will by way of example sometimes refer to stock prices,however, it is understood that the methodology is applicable to othersales of products, to all corporate financial instruments, such as, butnot limited to, bonds, currencies, commodities contracts, or indeedderivative constructs of those asset classes. Financial instruments willtypically be analyzed in conjunction with related instruments, e.g. inthe case of company stock prices together with the related industryindex. The methodology described herein is concerned with extending suchmodels of pricing financial instruments to include the impact of suddenfinancially sensitive events, such as, but not limited to, industrialaccidents, earnings warnings and product withdrawals. Depending on thefinancial instrument in question, the main media attention may notnecessarily involve companies, but could just as well relate togovernments or other organizations, commodities, and so forth. Themethodology comprises the following principal processes:

-   -   (1) Training: Compiling correlation reference material (FIG. 1)        -   Data collection and preparation (101)        -   Categorization of event classes and event parameters (102)        -   Population of event classes and event parameters (103)        -   Modeling the impact of media coverage around events on            financial instruments (104) and/or product sales        -   Evaluation of the modeling results and refining the model to            enhance the precision (105)    -   (2) Real-time Prediction (FIG. 6)        -   Evaluate coverage from news wires and broadcast immediately            following an event, in particular communication parameters            such as short term media coverage volume and tonal balance            (601)        -   Retrieve financial parameters of affected financial            instrument(s) (602)        -   Determine event parameters (603)        -   Model execution: Forecasts the short term and/or long term            impact of the event with particular consideration of the            effect on media coverage and the subsequent effect on the            short term price movement of the financial instrument(s)            (604)        -   Post-mortem analysis of the results and refinement of model            (606)

The building (104) and execution (604) of these models—numerical innature—require the properties of the financial instruments (and/orproduct sales) and media coverage to be captured in quantitativeparameters. Examples of communication parameters are volume of articlespublished per day, the weight of the publications in which the articlesare published, the tonality of the articles. Tonality is a parameter tocapture the sentiment expressed in the media, for example in determiningif an article on a particular issue as positive or negative from theperspective of the subject company. In order to apply tonality in thedisclosed methodology, it needs to be cast into quantitative terms.Further parameters may be the editorial attention time span for aspecific issue (e.g. number of days until messages are subsiding to 10%of maximum coverage per day), any reference to stock price impact inearly media coverage or any reference to other problems in the samecompany within the initial coverage. As used herein, the phrase “mediacoverage” may include any publicly available information from varioustypes of sources, including newspapers, radio, online reporting,magazines, news bulletins or feeds, press releases, television, courtfilings (e.g., including a court filing initiating a lawsuit, an appeal,or a judicial decision), blog postings, tweets, etc.

Financial parameters include the trading volume and price movement ofthe instrument itself (e.g. stocks), its derivatives (e.g. options), andbenchmarks (e.g. indices) as well as the underlying quantitative factorsincluding but not limited to products revenues, products pricing andprofit margins. Additional financial parameters such as the (abnormal)return or volatility over a given period can be calculated based on theprice movement and other primary financial parameters. Examples offurther parameters related or useful as context to the financialinstrument are overall market volatility, industry volatility,background economic data such as consumer confidence indices, houseprices and recent interest rate developments, % stock ownership ofindividuals and institutions, corporate profit/earnings benchmarkedagainst industry, long term dividend record and marketing statistics forthe sales of products/services in the relevant sector.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

Exemplary embodiments will be described in relation to the followingfigures wherein:

FIG. 1 illustrates exemplary processes for establishing a trained modelof an exemplary embodiment of the disclosed method;

FIG. 2 illustrates exemplary processes for data gathering, cleansing,and enrichment of an exemplary embodiment of the disclosed method;

FIG. 3 illustrates exemplary process for event identification,classification, and parameterization of an exemplary embodiment of thedisclosed method;

FIG. 4 illustrates exemplary process creating event instances withindividual event data of an exemplary embodiment of the disclosedmethod;

FIG. 5 illustrates exemplary processes for establishing a model fordetermining media influence of reported events in an exemplaryembodiment of the disclosed method;

FIG. 6 illustrates exemplary processes for outputting values based onanalysis using the model of FIG. 1 in an exemplary embodiment of thedisclosed method;

FIG. 7 illustrates an exemplary structural equation model relating eventparameters to output thereby forming part of the process of FIG. 6 in anexemplary embodiment of the disclosed method.

DETAILED DESCRIPTION OF THE INVENTION

For illustration of the principles and operational characteristics ofthis method it is helpful to present an example. The following exampleshows the method for building the model and training it for the separateprediction of the media volume/tonality and the stock price impactresulting from a product withdrawal.

The process is presented in the flowchart given in FIG. 1. The detaileddescription of the individual steps is presented herein. The processbegins with the data collection and preparation (101). Afterwards theevents are categorized into event classes and the parameters specific toeach of the particular event classes are determined (102). In thecurrent example the event class is “product withdrawal” and the eventparameters are given in Table 1 (A-J).

Next, a number of similar accidents in the past are analyzed. The datafrom the events are populated into the event parameters (103). The wayit is done for a specific event is illustrated in the prediction processexample provided herein. The content of Table 1 presents the parametersof three historical products withdrawals that have happened to thecompanies X, Y and Z.

TABLE 1 Company X Y Z A. Product sales/revenue 21   5% 41 B Expectedsales reduction 45 100  18 C. Fatalities  1  7 15 D. Casualties  0 30  6E.1. Initial media volume  3  5  6 E.2. Initial media tonality −9 −3 −6C. Crisis management quality  12%   0% 90 H. Expected litigation risk$OM $12  $I I. Event resonance  4  0  2 J. Event location weighting 100%  40%   0% K. Impact on media volume 215  428  35 L. Impact onmedia tonality −1 −5 −4 M. Business impact −9 −3 −6 N. Short term stockprice   −7.5% −11  —

The parameters A-J are the basis on which the prediction is based. Theinitial parameter set is event specific and is not limited to the itemsgiven in this example. In particular, the possible inclusion offinancially non-obvious parameters such as quality of photos (determinesto a large degree prominence of coverage) is noted here. For each of theinitial parameters specific normalization is applied. For example thenormalization of the expected litigation risk H_(N) is derived bydividing the litigation risk to the company's revenues: H_(N)=H/Revenuesand for the normalized value of Fatalities (C_(N)) we may use log(C).

The model is created by using factor analysis, structural equationmodeling, neural networks and other methods. For the correspondingexample the model is presented in FIG. 7. Using structural equationmodeling the following equations for expressing (1) the media coverageand (2) tonality; and (3) the short term stock price impact are derived:K _(N)=0.1A _(N)+0.3B _(N)+1.1C _(N)+0.3D _(N)+0.2E1_(N)−0.6E2_(N)+1.3F_(N)+0.1G _(N)+0.4H _(N)+0.2I _(N)  (1)L _(N)=−0.3A _(N)−0.2B _(N)−0.1C _(N)−0.3D_(N)−1.3E1_(N)+1.1E12_(N)−0.3F _(N)−0.5G _(N)−0.3H _(N)−0.31_(N)  (2)M _(N)=0.5A _(N)+0.2H _(N)  (3)N _(N)=1.4K _(N)−2.3L _(N)+4.8M _(N)  (4)

Training Process

An overview of the training process is given in FIG. 1, and thesub-processes depicted therein are described in more detail in thefollowing section.

Data collection and preparation (101 in FIG. 1 and entire FIG. 2): Thereare various aspects to the task of data preparation, such as datagathering, storage & indexing, cleansing, and enrichment. These tasksare typically carried out by a computer system with features such as:

-   -   Data gathering consists of retrieving media and financial data        (201 and 202, respectively) from different providers. An example        of an embodiment of the gathering process is as follows: a human        being specifies in a graphical user interface (GUI) of the        computer system the details of the desired data sets, such as        date ranges and companies. The computer system transforms the        user entry to send an HTTP request to the service provider,        where the URL used in the HTTP request contains the query        details such as the date ranges and desired objects. The service        provider responds with delivering (also via HTTP) an XML or        comma separated value (CSV) file (the news or financial feed).    -   Data storage & indexing (203) consists of parsing the retrieved        information and storing it in a structured way such as a        computer database. The computer database is either linked by a        computer network to or is part of the computer system that        gathered the data. The database needs to ensure by its design        the connection between various data sub-sets organized in        tables. For reasons set out in the description of the data        processing in the latter section of this document, it is        beneficial to store and index articles according to such data        elements as article title, the publication it appeared in, the        date of publication, as well as a summary or article “snippet”.    -   Data cleansing (204) consists of removing or adjusting parameter        values that would hinder the correct subsequent processing of        the data. While news is generated and dispersed through media        virtually every day of the year, this is not true for all        financial markets. While in many cases there is the possibility        to trade a financial instrument in after-hour markets, the main        exchanges are closed during weekends and public holidays.        Furthermore, sometimes a particular financial instrument of        interest is suspended from trading on a particular exchange. A        further integral part of the cleansing is to properly account        for stock splits and dividend payments.    -   Data enrichment (205) consists of adding data elements that were        not included in the data feed of the service providers. For        example in the current embodiment, the publications are        identified in the news feed by name, but their distribution        volume, geographical location of main readership, type of        publication such as general daily newspaper or specialist weekly        trade magazine are added to the database manually. Such a        categorization allows to classifying and weighting the        importance of articles by publication.

Categorization of event classes and event parameters (102 in FIG. 1 andentire FIG. 3): Given the data of historical events, and correspondingpress coverage and prices movements of financial instruments gathered inprocess 101, with a subsequent process 102 one can categorize theevents. The events of interest are related to a period of abnormalreturn of a financial instrument (stock price gain or loss drivenprimarily by the impact of the event rather than normal businessoperations/conditions). In a first sub-process 301 one can identify theabnormal return of a financial instrument by various filteringapproaches, e.g. filtering the returns above a certain value, either inabsolute terms or relative to a peer group (basket/index). In afollowing sub-process 302 the media coverage related to the abnormalreturn is being processed. Media coverage published at the time of theabnormal return is filtered for relevance to the abnormal return, forexample using filter criteria such as publication date and key-wordsearch of the object (company) experiencing the abnormal return. In afollowing sub-process 303, the event as covered in the media causing theabnormal return is identified and categorized into classes (e.g., inaccordance with a manual or automatic assessment of risk). Examples ofsuch events are, but are not limited to: Government Probes; ProductSafety; Product Approvals/Recalls; Terrorism; Resignations; IndustrialAccidents; Corruption/Bribes; Strikes; Significant Lawsuits (e.g., aclass action lawsuit).

After categorizing the events into classes, the corresponding parametersfor each class are extracted (304). See the possible, but by no meansexhaustive list of parameters in Table 1.

It is straight forward that significant events can strongly impact theprice of a related financial instrument. The example provided hereinshows how a particular product withdrawal event can be defined for thepurposes of this methodology with the parameters specified in Table 2.This categorization of events 303 and extraction of event parameters 304may be carried out by a human expert or alternatively/additionally bycomputer-aided clustering mechanisms such as self-organizing maps (SOM,single-layer rectangular-shaped neural networks with unsupervisedlearning). Where the human expert is judging out of experience andbusiness insight which parameters are relevant, the computer-aidedmechanisms look for commonalities of media coverage features in eachevent class to extract parameters as promising candidates. Thedefinition of the event classes and the event parameters can be refinedin a feedback loop via sub-process 105.

Population of event classes and event parameters (103 in FIG. 1 andentire FIG. 4): Once the event classes and event parameters have beendetermined, historical events can be mapped to these classes and theirevent parameter values can be extracted. In sub-process 401, for eachhistorical event the relevant media coverage is filtered out,identified, and allocated to the relevant event class. In sub-process402, the event parameter values for each historical event are beingextracted. The set of event parameters is specific to the event class,although more than one event class may use a particular parameter. In asub-process 403, the search criteria for this particular event are beingidentified and stored with search criteria of other historical events ofthe same class. The purpose of this sub-process 403 is to aid thedevelopment of automatic identification of media coverage of aparticular event class.

Modeling (104 in FIG. 1 and entire FIG. 5): A covariance analysis (501)is being carried out between said event parameters and parameters of thefinancial instruments. As the parameter values have been identified inthe preceding sub-processes, this analysis (501) can be carried outautomatically without any human interaction.

Based on those covariance values that indicate a high degree ofcorrelation and statistical significance, one can build or refine insub-process 502 a model of how the events and their media coverageinfluence the price movement of the financial instrument. The fields ofpsychometric and econometric modeling have provided known research onthe actual details of an analytical extraction of correlationcoefficients, and modeling cause and effects, for example via factoranalysis or structural equation models (SEM) that can contain exogenous,endogenous, observed, and latent variables. Alternatively, a morenumerically driven approach may be used with such tools as multilayeredfeed-forward neural networks (or similar) with supervised learning(back-propagation, simulated annealing, genetic algorithm, and others).The overall methodology for quantitative indication of the influence ofcertain events to stock price movements described herein is not limitedto one specific choice of correlation analysis, although the approaches'pros and cons with respect to this utility is an interesting field offurther research.

In a sub-process 503 the model is tested against historical data thathas not been set aside and not been used in the building of the model(502). As used herein, the phrase “historical data” might include, forexample, average damage awards associated with prior lawsuits. Adecision in sub-process 504 determines whether the model is deemed tofit the data sufficiently well, and the process Modeling (104) iscomplete, or whether any discrepancies in the fit are sufficiently largeas to warrant the refinement of the model in sub-process 502. Theexisting embodiment of the invention currently does not include anautomated feedback loop 504 to 502 whereby insights gained duringtesting are automatically adjusting the coefficients in the model.However, we aim to pursue such automated feedback loops in a furtherembodiment of the invention. Studies in particular in the field ofneural networks are describing various approaches of a feedbackimplementation.

Real-Time Prediction

As initially described in the overview section of this document, afterbuilding the numerical model its operational usage is to predict theinfluence of the event parameters on the media impact and businessimpact and their influence on the price movement of a financialinstrument. Moreover, predictions might be made about how much revenuewill be at risk or similar types of financial impact information (e.g.,in connection with an amount of a potential damage award associated witha class action lawsuit). Note that a predicted financial impact might beassociated with product sales (e.g., after a safety recall of theproduct or a critical report on a product's quality), a stock price, astock option price, a bond value, a credit default swap value, and/orother underlying instruments (e.g., a corporation's portfolio ofproducts and services that generate cash flow).

At the occurrence of a given event (for example a corruption case or alawsuit filed between two corporations), the user will retrieve in aprocess 601 initial media coverage of the event, and the financialparameters in process 602. The actual retrieval processes may well bethe same as described in the data collection process 101, with thedistinction of course that actual data on media coverage and pricemovement of the financial instrument only becomes available as the eventunfolds. In a following process 603 the user will then query a databaseof event classes and their related parameters. If the user finds asuitable event class for the given event, he will then extract therelated parameter values from the news about the current event insub-process 603. In sub-process 604, the parameter values are thenapplied in conjunction with the relevant communication and financialparameters to the numerical model (an example of how to apply thosevalues for prediction is given in the next paragraphs. This process 604is similar to the one described under model testing 503, only thatoutput 605 is the actual prediction upon which the user can act.

In order to illustrate the prediction process, a fictitious simpleexample is given: At 14:23 June 2007 a highly negative study ispublished on the side effects of a specific cancer drug named Extundum.The trials were conducted in the UK on a population of 1,000participants. The drug is owned and produced by InnovariumPharmaceutical Inc, listed on the New York stock exchange. Patients whosuffered side effects: 30 became ill (casualties), 13 died from thecomplications (fatalities). There is some increased sensitivity, asearly reports suggest that Innovarium may have hidden a previousinternal study. The sales of Extundum accounts for 18% of Innovarium'sglobal revenues, but accounts for 22% of Innovarium's revenues in themarket where the study came out (UK). The handling of the corporatecrisis is handled without any CEO visibility, but the corporate responseincludes the standard messages associated with best practice. Further,the UK's National Institute for Clinical Excellence (NICE) hadfast-tracked the drug's approval, and there is already an on-going andrising debate on the safety of the fast-tracking. NICE itself is in thenews for the slow standard approval process, which in turn created theneed for a fast-track arrangement.

The parameters that need to be extracted for the prediction (see FIG. 7)are the same parameters that have been used in the section of thetraining process in the example of a product withdrawal given therein.This paragraph illustrates an approach of how to extract said parameters(see Table 2 below with populated values for the parameters). Theimportance of the product is expressed as a percentage of product salesover total corporate revenue (this may be a vector accounting fordifferent figures in different markets). An expert takes a view on howlikely a full product withdrawal is (resulting in 100% loss of theproduct sale) in the various markets and the likelihood of continuedsales, albeit at reduced level due to the reputational damage. Thisleads to a value for the Expected Sales Reduction 701. Initial mediacoverage is measured by a vector of which news wires are picking up thestory and their tonality, multiplied with a weighting for the importanceof the news wire in the affected markets. In this example we weightAssociated Press with 5 (main wire for the US, where the company islisted on the stock exchange), and Reuters (main wire for Europe wherethe story breaks) with a 4. The quality of the corporate crisismanagement 704 is computed from the number of mentions that a companyspokesperson gets, the number of CEO interviews and citations, as wellas whether concern and apologies are expressed in the early mediacoverage of this event. These values are combined into a single score tobe put into the model. 705 and 706 are the number of fatalities andcasualties in this event, respectively. Resonance 707 is a parameterthat takes into account whether there is an ongoing public concern thatpredates this event and which is likely to amplify the discussion aroundthis event. In this example, this is the case with the debate on safetyof fast-track drug approval processes. An embodiment for deriving thisparameter value is the multiplication of the explicit references in themedia coverage of the event times the number of articles about thepublic concern in the past 6 months. The event location weightingparameter 708 accounts for the weighted distance of the event to themain trading market of the financial instrument (=100% for an event inthe US for a US listed company, 70% for a pan-European event for a USlisted company). See Table 2 below for an overview of the parameters andtheir values.

TABLE 2 Company Innovarium A. Product sales/revenue  18% B. Expectedsales reduction    3.5% C. Fatalities 13 D. Casualties 30 E.1. Initialmedia volume  6 E.2. Initial media tonality −2 G. Crisis managementquality  12% H. Expected litigation risk $7M I. Event resonance 76 J.Event location weighting  70% K. Impact on media volume 215  L. Impacton media tonality −4 M. Business impact −9 N. Short term stock price −16%

Based on these parameters the model can compute its intermediate andfinal results. The intermediate results are the expected media coveragevolume/tonality and the business impact of the event.

The media and business intermediate results lead to a final predictionof a stock price forecast of −14% to −18% against a normal return.

Subsequently, the actual price movement data will be available, and theprediction can be analyzed against this benchmark in a post-mortemanalysis 606. The process of analyzing and model refining is similar asdescribed under 504.

Once the parameters have been provided, the predictive models willproduce predictions for the next day and for subsequent time points. Thelength of time between the time points may be varied for different eventtypes. The model will also provide a confidence coefficient for thepredictions, expressing the error variances.

The model may be applied for predictions at different points in time, insuch a way that different coefficients are used for each of thetime-point predictions, even for the same parameter. By way of example;the model to predict the media tonality prediction for day 2 differs inkey respects from that used to predict the media tonality for day 28.Different coefficients are also used to predict the different types ofoutputs for the same time point.

In the final step the minimum and maximum values for each predictedfactor are calculated, for each of the time points and for each of theoutput factors.

Predictions may be distributed to clients (e.g., in the form of a table)for any of the following business purposes:

-   -   Seeking short and/or long term capital gain through buying or        selling stocks or bonds in the subject company, including        short-selling.    -   This could be provided by offering integration into other equity        valuation models, or by supplying a black-box trading facility    -   Crisis management training, planning and preparations    -   Service may either be provided as one-off reports or an ongoing        subscription.

What is claimed:
 1. A computerized method, comprising: identifying an event having an association with a financial instrument; classifying, by a modeling computer system, said event into at least one of a plurality of predefined event classes, each predefined event class being associated with a set of similar events; retrieving, by the modeling computer system, media data associated with media coverage of said event and extracting data elements from said media data, wherein said data elements include at least one quantified communication parameter including at least one of a short term media coverage volume, a publication weight, a tonal balance, and an impact of available photographs; retrieving, by the modeling computer system, current financial parameters associated with said financial instrument; generating, by the modeling computer system as an intermediate result of the method, a prediction of upcoming media coverage of said event including a predicted volume and tonality of said upcoming media coverage, wherein said intermediate result is generated using the modeling computer system, a numerical model, said extracted data elements, information about said predefined event class, and said current financial parameters; generating, using the modeling computer system and using a numerical model and said extracted data elements, information about said predefined event class, said current financial parameters and said intermediate result, a predicted financial impact of said event; and outputting, from the modeling computer system, the predicted financial impact of said event.
 2. The computerized method of claim 1, wherein the event is associated with a court filing.
 3. The computerized method of claim 1, wherein the predicted financial impact of said event is associated with at least one of: (i) a price of said financial instrument, (ii) an amount of financial risk associated with the event, or (iii) an amount of revenue associated with the event.
 4. The computerized method of claim 1, wherein said generating an intermediate output of said numerical model further comprises generating a predicted business impact of said event.
 5. The computerized method of claim 1, wherein said at least one of a plurality of predefined event classes is at least one of: government probe, product safety, product approval, product recall, terrorism, resignation, industrial accident, corruption, bribe, strike, lawsuit, licensing, patent award, patent expiration, regulatory event, legislative change, boycott, accounting irregularity, prosecution, discrimination, insider trading, corporate manslaughter, injury or death related to product safety, incapacity of corporate officer, stock market delisting, stock market suspension, or resignation.
 6. The computerized method of claim 1, wherein said plurality of predefined event classes are created in a model training procedure, said model training procedure comprising: receiving historical data associated with a plurality of events, said historical data including media data and financial data; and analyzing said historical data to identify said plurality of predefined event classes.
 7. The method of claim 6, wherein said analyzing comprises at least one of: (i) an automated process or (ii) a manual process.
 8. The computerized method of claim 6, further comprising at least one method of measuring direction and strength of relationships and dependencies between variables and thus correlating said event and said historical data.
 9. The computerized method of claim 1, wherein said financial instrument is at least one of a company stock, a corporate bond, a government bond, a commodity, and a derivative instrument.
 10. The computerized method of claim 1, wherein said predicted financial impact of said event includes a predicted short term price movement of said financial instrument.
 11. The computerized method of claim 1, wherein said generating, using a numerical model, a predicted financial impact of said event further comprises augmenting said current financial parameters of said financial instrument with additional financial data including at least one of: (i) financial data associated with the movement of an index associated with the industry associated with said financial instrument; and (ii) financial data associated with the movement of an index associated with the market associated with said financial instrument.
 12. The computerized method of claim 11, further comprising: comparing normalized price movement data across at least one of an industry, a market and an asset class; and filtering out market influences not related to said event.
 13. The computerized method of claim 8, further comprising: repeating said retrieving media data, said retrieving current financial parameters, said determining a correlation, said predicted business impact, and said predicted financial impact over a period of time; and updating said numerical model based on said repeating.
 14. The computerized method of claim 8, wherein said determining a correlation uses quantitative parameters to compare and normalize different ones of said events within each of said predetermined event classes.
 15. The computerized method of claim 8, wherein said determining a correlation uses at least one of structural equation modeling (SEM), path analysis, and factor analysis.
 16. The computerized method of claim 1, wherein each of said predefined event classes includes at least a first parameter, said parameters extracted from historical data using at least one of a self-organizing map (SOM) and a clustering method.
 17. The computerized method of claim 8, wherein said determining a correlation includes learning a correlation via back-propagation and said correlation is utilized for classification and prediction using multilayered feed-forward networks.
 18. A computerized method, comprising: identifying, by a modeling computer system, an event having an association with a financial instrument; classifying, by the modeling computer system, said event into at least one of a plurality of predefined event classes, each predefined event class being associated with a set of similar events; retrieving, by the modeling computer system, media data associated with media coverage of said event and extracting data elements from said media data, wherein said data elements include at least one quantified communication parameter including at least one of a short term media coverage volume, a publication weight, a tonal balance, and an impact of available photographs; generating a prediction of upcoming media coverage of said event including a predicted volume and tonality of said upcoming media coverage, wherein said prediction is generated using the modeling computer system, a numerical model, said extracted data elements, and information about said predefined event class; and outputting, from the modeling computer system, the predication of the upcoming media coverage including the predicted volume and tonality of said upcoming media coverage.
 19. The method of claim 18, further comprising: generating, using the modeling computer system, said extracted data elements, information about said predefined event class, current financial parameters and said prediction of upcoming media coverage, a predicted financial impact of said event.
 20. The method of claim 19, wherein the predicted financial impact is associated with at least one of: (i) product sales, (ii) a stock price, (ii) a stock option price, (iii) a bond value, or (iv) a credit default swap value. 